The field of the invention is the detection of brain disorders using functional magnetic resonance imaging (fMRI) techniques, and particularly the detection of Alzheimer's disease.
Alzheimer's disease is a devastating disease of the brain which results in progressive dementia, physical disability and death over a relatively long period of time. With the aging population in the United States and other countries, the number of Alzheimer's patients is rapidly rising and can accurately be characterized as a silent epidemic. Much research is being conducted to develop drugs that will slow or halt the progression of the disease, and there is hope that a vaccine or inhibitors of secretase may ultimately be developed.
One of the difficulties in managing this disease is the lack of means for its early detection and means for measuring its progression. Such means are needed to identify persons who should receive treatment and to measure the effectiveness of the treatment. An immediate problem is the need for a method which measures the progression of the disease in order to evaluate the effectiveness of the many drugs being developed.
Many techniques have been proposed for detecting and measuring the progress of Alzheimer's disease. These include cognitive tests which attempt to measure brain functions by having the patient perform different tasks. The problem with this approach is that it does not distinguish between dementia caused by Alzheimer's disease and dementia caused by other factors. In addition, the ability to measure the progression of the disease using cognitive tests is very limited.
Neurofibrillary tangles (NFTs) and neuritic plaques (NPs) are the classical neuropathological hallmarks of Alzheimer's disease. Numerous neuropathological studies indicate that the first appearance of NFTs and NPs in the hippocampal region of the brain marks the beginning of the degenerative process. Many studies have been done in which the structure of the brain has been imaged to determine structural changes that are linked to the presence and the progression of Alzheimer's disease. These include: 2-D estimates of size; measures of medial temporal lobe gray matter volume; the qualitative rating of the amount of CSF accumulating in the hippocampal fissures, the size of the suprasellar cistern; and the increased distance between the right and left uncus. None have been particularly successful, and in fact, it has been found that profound structural changes can occur in the brain of some individuals with no cognitive impairment or other symptoms of the disease being evident.
It has been suggested that with progression of Alzheimer's disease, the increased presence of NFTs and NPs in the hippocampus disrupt the perforant pathway and affect functional connectivity. A number of methods have been proposed to assess the functional connectivity in the hippocampal region. The concept of functional connectivity is widely applied to electroencephalogram (EEG) coherence, where it is a measure of the synchronization between two signals across distinct regions of the human brain and is interpreted as an expression of their functional interaction. In positron emission tomography studies, functional connectivity is defined as a spatiotemporal correlation between spatially distinct regions of cerebral cortex. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be used to monitor regional cerebral glucose metabolism (rCMRglc) and regional cerebral blood flow (rCBF). It has been found that significant hypoperfusion and hypometablism occur in the region of temporal and parietal association cortices in probable Alzheimer's patients. Many studies have demonstrated the correlation between regional localized hypoperfusion and hypometablism with cognitive deficits seen on behavioral testing. Despite frequent reports of abnormal function in Alzheimer's patients observed by PET and SPECT, however, the clinical utility of these methods is still controversial.
Functional magnetic resonance imaging (fMRI) technology provides a new approach to study neuronal activity. Conventional fMRI detects changes in cerebral blood volume, flow, and oxygenation that locally occur in association with increased neuronal activity induced by functional paradigms. As described in U.S. Pat. No. 5,603,322, an MRI system is used to acquire signals from the brain over a period of time. As the brain performs a task, these signals are modulated synchronously with task performance to reveal which regions of the brain are involved in performing the task. Much research has been done to find tasks which can be performed by patients, and which reveal in an fMRI image acquired at the same time, regions in the brain that function differently when Alzheimer's disease is present.
In U.S. Pat. No. 6,490,472 an fMRI method is described for producing an indication of the presence and the progress of a brain disorder by measuring the functional connectivity at different locations while the brain is substantially at rest. An index called the “COSLOF” index is calculated from the time course fMRI data acquired from a selected region of the brain and a strong correlation was found between this index and the presence of Alzheimer's disease.
The COSLOF index method has two limitations. First, it is very difficult to accurately identify the voxels in the brain that should be included in the calculation of the index. And second, the SNR of the acquired BOLD signal is very low and this impairs the accurate calculation of the COSLOF index. As a result, the COSLOF index is useful in identifying patients with Alzheimer's disease, but it is not sensitive enough to identify patients with mild cognitive impairment (“MCI”) who will eventually evidence Alzheimer's disease. The identification of MCI patients is very important, since it is these patients who should receive treatment before Alzheimer's disease more significantly impairs the brain.